ETC5543 – Business Analytics Creative Activity
14 October 2025
Figure 2
Pre-processing (used by the RNN):
Size-standardise each fish to 450 mm with offset_dB = 10*log10(450/length)
Convert F45–F170 from dB → linear backscatter: exp((dB + offset_dB)/10)
Grouped splits by fishNum (train / valid / test)
| Model | Acc @ 0.50 | Policy thr | Acc @ Policy |
|---|---|---|---|
| RNN (reproduction on 5-ping blocks) | 0.593 | — | — |
| AutoML (per-ping, original data) | 0.668 | 0.7000 | 0.663 |
Four variants
| Variant | Acc @ 0.50 | Policy thr | Acc @ Policy |
|---|---|---|---|
| QUINTILES_ALLFREQ | 0.883 | 0.4754 | 0.867 |
| QUINTILES_FEATS | 0.633 | 0.4000 | 0.750 |
| MEDIAN_ALLFREQ | 0.917 | 0.5796 | 0.833 |
| MEDIAN_FEATS | 0.667 | 0.4000 | 0.750 |
OOF (Out-of-Fold) Threshold Tuning – to optimise classification thresholds
Model Hyperparameter Tuning – for the Deep Learning grid (layers, dropout, epochs, learning rate, etc.).
Use discriminative frequencies
Select F* bands that best separate LT vs SMB.
→ Train compact models on these top F* only to reduce noise & overfitting.
Add richer time-series features from fabletools/feasts
Every fish leaves a sonic fingerprint. Our job is to read it.
Fish Hydroacoustics — ETC5543
ETC5543 — Fish Hydroacoustics